The creation and interactive visualization of artificial computer graphics (CG) environments is an important application in the field of computer graphics. Many applications, such as CAD, architectural walkthroughs, simulations, medical visualization and computer games include interactive navigation, i.e., being able to move around a computer model/scene at greater than 10 frames per second.
A common trend within the field of interactive computer graphics is the increasing amount of CG datasets. Large CG datasets require specialized graphics systems used to accelerate the process. However, models exist that cannot be rendered at interactive speeds even with current high-end computer hardware. The development of computer hardware is not likely to solve the described problems since the size of the CG data and the size of the secondary computer memory is increasing at faster rates than the development of multimedia hardware.
In applications where the large CG datasets also have to be created, as in the case of computer games, the creation of the large datasets is an additional difficulty to the described trend. The original creation of graphics datasets is a two-step method, where in the initial stage, CG data is created in a non ordered fashion, or in a fashion that is not optimized for rendering. In a second stage, the data is ordered and optimized for rendering. This two-step method can be an inhibiting factor on the creation of large datasets, as the CG creators will not be able to interactively render the CG dataset while editing it.
Large CG datasets thus require algorithmic techniques to accelerate the creation and rendering process. Such techniques attempt to render at interactive speeds by either substituting simpler approximations for portions of the dataset or ignoring parts of it that are not used at that moment. The goal is to enable interactive CG of large CG datasets without significant degradation in final quality, or the possibility to trade quality for rendering speeds.